1. Field of the Invention
The present invention relates to providing an implementation of Support Vector Machines functionality integrated into a relational database management system
2. Description of the Related Art
Support Vector Machines (SVM) is a state-of-the-art data mining algorithm which has been used with great success in challenging domains such as text mining and life sciences. However, there are a number of problems with conventional implementations of SVM. For example, in conventional implementations, users have to export their data outside of the database and then use stand-alone packages to build and score SVM models. This approach is inefficient, time consuming, and compromises data security.
Another problem with conventional implementations is that building SVM models represents a significant challenge to the inexperienced user. Often, in order to produce a reasonable model, extensive parameter tuning is required. The users have to search the parameter space—usually via a grid search—for a combination of values that produces satisfactory accuracy and/or scoring performance. This is a time consuming and laborious process exacerbated by the fact that these parameters interact. In addition, building SVM models requires considerable system resources (memory and CPU). This problem is typically solved by learning on subsets (chunks) of the data. These chunks are updated at every iteration and the model build continues until the convergence conditions on the entire dataset are met. Usually chunks are composed from the examples that violate convergence conditions to the greatest extent. Finding the worst violators is computationally expensive. Additionally, this approach can result in oscillations that slow down the build process significantly.
A need arises for a technique by which SVM may be implemented that improves efficiency, time consumption, and data security, which reduces the parameter tuning challenges presented to the inexperienced user, and which reduces the computational costs of building SVM models.